A pilot project harnessing surveillance systems to support clinicians providing clinical care for people diagnosed with hepatitis C in Victoria, Australia, September 2021 to 31 March 2022.

BackgroundActive follow-up of chronic hepatitis C notifications to promote linkage to care is a promising strategy to support elimination.AimThis pilot study in Victoria, Australia, explored if the Department of Health could follow-up on hepatitis C cases through their diagnosing clinicians, to assess and support linkage to care and complete data missing from the notification.MethodsFor notifications received between 1 September 2021 and 31 March 2022 of unspecified hepatitis C cases (i.e. acquired > 24 months ago or of unknown duration), contact with diagnosing clinicians was attempted. Data were collected on risk exposures, clinical and demographic characteristics and follow-up care (i.e. HCV RNA test; referral or ascertainment of previous negative testing or treatment history). Reasons for unsuccessful doctor contact and gaps in care provision were investigated. Advice to clinicians on care and resources for clinical support were given on demand.ResultsOf 513 cases where information was sought, this was able to be obtained for 356 (69.4%). Reasons for unsuccessful contact included incomplete contact details or difficulties getting in touch across three attempts, particularly for hospital diagnoses. Among the 356 cases, 307 (86.2%) had received follow-up care. Patient-management resources were requested by 100 of 286 contacted diagnosing clinicians.ConclusionsMost doctors successfully contacted had provided follow-up care. Missing contact information and the time taken to reach clinicians significantly impeded the feasibility of the intervention. Enhancing system automation, such as integration of laboratory results, could improve completeness of notifications and support further linkage to care where needed.

e3SIM: epidemiological-ecological-evolutionary simulation framework for genomic epidemiology.

Infectious disease dynamics are driven by the complex interplay of epidemiological, ecological, and evolutionary processes. Accurately modeling these interactions is crucial for understanding pathogen spread and informing public health strategies. However, existing simulators often fail to capture the dynamic interplay between these processes, resulting in oversimplified models that do not fully reflect real-world complexities in which the pathogen's genetic evolution dynamically influences disease transmission. We introduce the epidemiological-ecological-evolutionary simulator (e3SIM), an open-source framework that concurrently models the transmission dynamics and molecular evolution of pathogens within a host population while integrating environmental factors. Using an agent-based, discrete-generation, forward-in-time approach, e3SIM incorporates compartmental models, host-population contact networks, and quantitative-trait models for pathogens. This integration allows for realistic simulations of disease spread and pathogen evolution. Key features include a modular and scalable design, flexibility in modeling various epidemiological and population-genetic complexities, incorporation of time-varying environmental factors, and a user-friendly graphical interface. We demonstrate e3SIM's capabilities through simulations of realistic outbreak scenarios with SARS-CoV-2 and Mycobacterium tuberculosis, illustrating its flexibility for studying the genomic epidemiology of diverse pathogen types.

Splenic marginal zone B cells restrict Mycobacterium tuberculosis infection by shaping the cytokine pattern and cell-mediated immunity.

Understanding the role of B cells in tuberculosis (TB) is crucial for developing new TB vaccines. However, the changes in B cell immune landscapes during TB and their functional implications remain incompletely explored. Using high-dimensional flow cytometry to map the immune landscape in response to Mycobacterium tuberculosis (Mtb) infection, our results show an accumulation of marginal zone B (MZB) cells and other unconventional B cell subsets in the lungs and spleen, shaping an unconventional B cell landscape. These MZB cells exhibit activated and memory-like phenotypes, distinguishing their functional profiles from those of conventional B cells. Notably, functional studies show that MZB cells produce multiple cytokines and contribute to systemic protection against TB by shaping cytokine patterns and cell-mediated immunity. These changes in the immune landscape are reversible upon successful TB chemotherapy. Our study suggests that, beyond antibody production, targeting the regulatory function of B cells may be a valuable strategy for TB vaccine development.

Terrestrial laser scanning and low magnetic field digitization yield similar architectural coarse root traits for 32-year-old Pinus ponderosa trees.

BACKGROUND: Understanding how trees develop their root systems is crucial for the comprehension of how wildland and urban forest ecosystems plastically respond to disturbances such as harvest, fire, and climate change. The interplay between the endogenously determined root traits and the response to environmental stimuli results in tree adaptations to biotic and abiotic factors, influencing stability, carbon allocation, and nutrient uptake. Combining the three-dimensional structure of the root system, with root morphological trait information promotes a robust understanding of root function and adaptation plasticity. Low Magnetic Field Digitization coupled with AMAPmod (botAnique et Modelisation de l'Architecture des Plantes) software has been the best-performing method for describing root system architecture and providing reliable measurements of coarse root traits, but the pace and scale of data collection remain difficult. Instrumentation and applications related to Terrestrial Laser Scanning (TLS) have advanced appreciably, and when coupled with Quantitative Structure Models (QSM), have shown some potential toward robust measurements of tree root systems. Here we compare, we believe for the first time, these two methodologies by analyzing the root system of 32-year-old Pinus ponderosa trees. RESULTS: In general, at the total root system level and by root-order class, both methods yielded comparable values for the root traits volume, length, and number. QSM for each root trait was highly sensitive to the root size (i.e., input parameter PatchDiam) and models were optimized when discrete PatchDiam ranges were specified for each trait. When examining roots in the four cardinal direction sectors, we observed differences between methodologies for length and number depending on root order but not volume. CONCLUSIONS: We believe that TLS and QSM could facilitate rapid data collection, perhaps in situ, while providing quantitative accuracy, especially at the total root system level. If more detailed measures of root system architecture are desired, a TLS method would benefit from additional scans at differing perspectives, avoiding gravitational displacement to the extent possible, while subsampling roots by hand to calibrate and validate QSM models. Despite some unresolved logistical challenges, our results suggest that future use of TLS may hold promise for quantifying tree root system architecture in a rapid, replicable manner.

PANAMA-enabled high-sensitivity dual nanoflow LC-MS metabolomics and proteomics analysis.

High-sensitivity nanoflow liquid chromatography (nLC) is seldom employed in untargeted metabolomics because current sample preparation techniques are inefficient at preventing nanocapillary column performance degradation. Here, we describe an nLC-based tandem mass spectrometry workflow that enables seamless joint analysis and integration of metabolomics (including lipidomics) and proteomics from the same samples without instrument duplication. This workflow is based on a robust solid-phase micro-extraction step for routine sample cleanup and bioactive molecule enrichment. Our method, termed proteomic and nanoflow metabolomic analysis (PANAMA), improves compound resolution and detection sensitivity without compromising the depth of coverage as compared with existing widely used analytical procedures. Notably, PANAMA can be applied to a broad array of specimens, including biofluids, cell lines, and tissue samples. It generates high-quality, information-rich metabolite-protein datasets while bypassing the need for specialized instrumentation.

Visual Intratumor Heterogeneity and Breast Tumor Progression.

High intratumoral heterogeneity is thought to be a poor prognostic indicator. However, the source of heterogeneity may also be important, as genomic heterogeneity is not always reflected in histologic or 'visual' heterogeneity. We aimed to develop a predictor of histologic heterogeneity and evaluate its association with outcomes and molecular heterogeneity. We used VGG16 to train an image classifier to identify unique, patient-specific visual features in 1655 breast tumors (5907 core images) from the Carolina Breast Cancer Study (CBCS). Extracted features for images, as well as the epithelial and stromal image components, were hierarchically clustered, and visual heterogeneity was defined as a greater distance between images from the same patient. We assessed the association between visual heterogeneity, clinical features, and DNA-based molecular heterogeneity using generalized linear models, and we used Cox models to estimate the association between visual heterogeneity and tumor recurrence. Basal-like and ER-negative tumors were more likely to have low visual heterogeneity, as were the tumors from younger and Black women. Less heterogeneous tumors had a higher risk of recurrence (hazard ratio = 1.62, 95% confidence interval = 1.22-2.16), and were more likely to come from patients whose tumors were comprised of only one subclone or had a TP53 mutation. Associations were similar regardless of whether the image was based on stroma, epithelium, or both. Histologic heterogeneity adds complementary information to commonly used molecular indicators, with low heterogeneity predicting worse outcomes. Future work integrating multiple sources of heterogeneity may provide a more comprehensive understanding of tumor progression.

Income dynamics and risk of colorectal cancer in individuals with type 2 diabetes: a nationwide population-based cohort study.

BACKGROUND: Individuals with type 2 diabetes (T2D) have increased colorectal cancer (CRC) risk, but it is unknown whether income dynamics are associated with CRC risk in these individuals. We examined whether persistent low- or high-income and income changes are associated with CRC risk in non-elderly adults with T2D. METHODS: Using nationally representative data from the Korean Health Insurance Service database, 1,909,492 adults aged 30 to 64 years with T2D and no history of cancer were included between 2009 and 2012 (median follow-up of 7.8 years). We determined income levels based on health insurance premiums and assessed annual income quartiles for the baseline year and the four preceding years. Hazard ratios(HRs) and 95% confidence intervals(CIs) were estimated after adjusting for sociodemographic factors, CRC risk factors, and diabetes duration and treatment. RESULTS: Persistent low income (i.e., lowest income quartile) was associated with increased CRC risk (HRn=5years vs. n=0years 1.11, 95% CI 1.04-1.18; P for trend=0.004). Income declines (i.e., a decrease≥25% in income quantile) were also associated with increased CRC risk (HR≥2 vs. 0 declines 1.10, 95% CI 1.05-1.16; p for trend=0.001). In contrast, persistent high income (i.e., highest income quartile) was associated with decreased CRC risk (HRn=5years vs. n=0years 0.81, 95% CI 0.73-0.89; p for trend<0.0001), which was more pronounced for rectal cancer (HR 0.64, 95% CI 0.53-0.78) and distal colon cancer (HR 0.70, 95% CI 0.57-0.86). CONCLUSIONS: Our findings underscore the need for increased public policy awareness of the association between income dynamics and CRC risk in adults with T2D.

Generation of antitumor chimeric antigen receptors incorporating T cell signaling motifs.

Chimeric antigen receptor (CAR) T cells have been used to successfully treat various blood cancers, but adverse effects have limited their potential. Here, we developed chimeric adaptor proteins (CAPs) and CAR tyrosine kinases (CAR-TKs) in which the intracellular ζ T cell receptor (TCRζ) chain was replaced with intracellular protein domains to stimulate signaling downstream of the TCRζ chain. CAPs contain adaptor domains and the kinase domain of ZAP70, whereas CAR-TKs contain only ZAP70 domains. We hypothesized that CAPs and CAR-TKs would be more potent than CARs because they would bypass both the steps that define the signaling threshold of TCRζ and the inhibitory regulation of upstream molecules. CAPs were too potent and exhibited high tonic signaling in vitro. In contrast, CAR-TKs exhibited high antitumor efficacy and significantly enhanced long-term tumor clearance in leukemia-bearing NSG mice as compared with the conventional CD19-28ζ-CAR-T cells. CAR-TKs were activated in a manner independent of the kinase Lck and displayed slower phosphorylation kinetics and prolonged signaling compared with the 28ζ-CAR. Lck inhibition attenuated CAR-TK cell exhaustion and improved long-term function. The distinct signaling properties of CAR-TKs may therefore be harnessed to improve the in vivo efficacy of T cells engineered to express an antitumor chimeric receptor.

Model-Based Analysis of Arsenic Retention by Stimulated Iron Mineral Transformation under Coastal Aquifer Conditions.

A multitude of geochemical processes control the aqueous concentration and transport properties of trace metal contaminants such as arsenic (As) in groundwater environments. Effective As remediation, especially under reducing conditions, has remained a significant challenge. Fe(II) nitrate treatments are a promising option for As immobilization but require optimization to be most effective. Here, we develop a process-based numerical modeling framework to provide an in-depth understanding of the geochemical mechanisms controlling the response of As-contaminated sediments to Fe(II) nitrate treatment. The analyzed data sets included time series from two batch experiments (control vs treatment) and effluent concentrations from a flow-through column experiment. The reaction network incorporates a mixture of homogeneous and heterogeneous reactions affecting Fe redox chemistry. Modeling revealed that the precipitation of the Fe treatment caused a rapid pH decline, which then triggered multiple heterogeneous buffering processes. The model quantifies key processes for effective remediation, including the transfer of aqueous As to adsorbed As and the transformation of Fe minerals, which act as sorption hosts, from amorphous to more stable phases. The developed model provides the basis for predictions of the remedial benefits of Fe(II) nitrate treatments under varying geochemical and hydrogeological conditions, particularly in high-As coastal environments.

Detection of Changes in Soil Microbial Community Physiological Profiles in Relation to Forest Types and Presence of Antibiotics Using BIOLOG EcoPlate.

Soil is home to microbiota with diverse metabolic activities. These microorganisms play vital roles in many ecological processes. Thus, the assessment of microbial functional diversity is an important quality indicator of soil ecosystems. In this study, we collected soil samples from three distinct forest habitats, i.e., an agroforest, a primary forest (PF), and a secondary forest, within the Angat Watershed Reservation in Bulacan, Northern Philippines. Community-level physiological profiling (CLPP) was done with the BIOLOG EcoPlate™ to analyze the responses of the soil microbial communities from the three forest habitats in the absence or presence of antibiotics. The BIOLOG EcoPlate represents 31 utilizable carbon sources. Based on the CLPP analysis, soil samples from the PF showed significantly higher utilization of most carbon sources than the other forest types (p < 0.05). Thus, less disturbed forest types constitute more functionally diverse microbial communities. The presence of antibiotics significantly decreased the carbon utilization patterns of the soil microbial communities (p < 0.05), indicating the possible use of CLPP in monitoring contamination in soil.

Lisfranc Open Reduction and Internal Fixation.

VIDEO AVAILABLE AT: https://ota.org/education/ota-online-resources/video-library-procedures-techniques/lisfranc-open-reduction-and.

Measuring cognitive effort using tabular transformer-based language models of electronic health record-based audit log action sequences.

OBJECTIVES: To develop and validate a novel measure, action entropy, for assessing the cognitive effort associated with electronic health record (EHR)-based work activities. MATERIALS AND METHODS: EHR-based audit logs of attending physicians and advanced practice providers (APPs) from four surgical intensive care units in 2019 were included. Neural language models (LMs) were trained and validated separately for attendings' and APPs' action sequences. Action entropy was calculated as the cross-entropy associated with the predicted probability of the next action, based on prior actions. To validate the measure, a matched pairs study was conducted to assess the difference in action entropy during known high cognitive effort scenarios, namely, attention switching between patients and to or from the EHR inbox. RESULTS: Sixty-five clinicians performing 5 904 429 EHR-based audit log actions on 8956 unique patients were included. All attention switching scenarios were associated with a higher action entropy compared to non-switching scenarios (P < .001), except for the from-inbox switching scenario among APPs. The highest difference among attendings was for the from-inbox attention switching: Action entropy was 1.288 (95% CI, 1.256-1.320) standard deviations (SDs) higher for switching compared to non-switching scenarios. For APPs, the highest difference was for the to-inbox switching, where action entropy was 2.354 (95% CI, 2.311-2.397) SDs higher for switching compared to non-switching scenarios. DISCUSSION: We developed a LM-based metric, action entropy, for assessing cognitive burden associated with EHR-based actions. The metric showed discriminant validity and statistical significance when evaluated against known situations of high cognitive effort (ie, attention switching). With additional validation, this metric can potentially be used as a screening tool for assessing behavioral action phenotypes that are associated with higher cognitive burden. CONCLUSION: An LM-based action entropy metric-relying on sequences of EHR actions-offers opportunities for assessing cognitive effort in EHR-based workflows.

Machine Learning-Assisted Precision Manufacturing of Atom Qubits in Silicon.

Donor-based qubits in silicon, manufactured using scanning tunneling microscope (STM) lithography, provide a promising route to realizing full-scale quantum computing architectures. This is due to the precision of donor placement, long coherence times, and scalability of the silicon material platform. The properties of multiatom quantum dot qubits, however, depend on the exact number and location of the donor atoms within the quantum dots. In this work, we develop machine learning techniques that allow accurate and real-time prediction of the donor number at the qubit site during STM patterning. Machine learning image recognition is used to determine the probability distribution of donor numbers at the qubit site directly from STM images during device manufacturing. Models in excess of 90% accuracy are found to be consistently achieved by mitigating overfitting through reduced model complexity, image preprocessing, data augmentation, and examination of the intermediate layers of the convolutional neural networks. The results presented in this paper constitute an important milestone in automating the manufacture of atom-based qubits for computation and sensing applications.

The Carrier Proteome Should Be Reassessed for Each Mass Analyzer Architecture.

A clever utilization of classic proteomics reagents now allows the effective amplification of the peptide sequencing potential in shotgun proteomics. The application of this method has helped usher in the exciting new field of single cell proteomics. While it was easy to first think that this approach was finally the answer for the polymerase chain reaction in protein chemistry, limitations were carefully described by the authors and others. A study by Cheung et al. systematically identified the consequences of higher concentration carrier proteomes and defined the "carrier proteome limit" [Cheung et al. Nat. Methods 2021, 18, 76]. While this work has been replicated by others, every analysis published to date has used a variation of the same mass analyzer. When the same analysis is performed on alternative instruments, these limits appear to be very different and can be attributed to defined characteristics of each mass analyzer. Specifically, in mass analyzers with a higher relative intrascan linear dynamic range, increased carrier channels appear less detrimental to quantitative accuracy. As such, we may be limiting the power of isobaric peptide signal "amplification" by restricting ourselves to traditional mass analyzer options for shotgun proteomics.

Infection rates during eras of intrathecal antibiotic use followed by antibiotic-impregnated catheter use in prevention of cerebrospinal fluid shunt infection.

OBJECTIVE: The Hydrocephalus Clinical Research Network (HCRN) implemented a perioperative infection prevention bundle for all CSF shunt surgeries in 2007 that included the relatively unproven technique of intrathecal instillation of the broad-spectrum antibiotics vancomycin and gentamicin into the shunt. In the meantime, the field debated the use of antibiotic-impregnated catheter (AIC) shunt tubing using clindamycin and rifampin, an increasingly widespread, but expensive and controversial, technique. It is unknown whether there were changes in infecting organisms associated with the use of these techniques during CSF shunt surgery at the hospital level. Key comparison periods include during the use of intrathecal antibiotics (period 1 from June 1, 2007, to December 31, 2011, at HCRN hospitals) and AIC (period 2 from January 1, 2012, to December 31, 2015, at HCRN as well as increasing over time at non-HCRN hospitals) and only standard use of routine prophylactic antibiotics (period 1 at non-HCRN hospitals). The aim of this study was to examine rates of CSF shunt surgery-related infections from 2007 to 2012 at the hospital level, including HCRN and non-HCRN hospitals, with a focus on infections with gram-negative organisms. METHODS: The authors conducted a retrospective observational cohort study at 6 children's hospitals with enrollment from 2007 to 2012 and surveillance through 2015. Bimonthly rates of shunt surgery-related infections were summarized to produce an overall hospital-specific time series, as well as by HCRN/non-HCRN status. An interrupted time series analysis was performed to assess the impact of change in HCRN perioperative infection prevention bundle on overall bimonthly infection rates. Quarterly rates of gram-negative shunt surgery-related infections were summarized to produce an overall hospital-specific time series. RESULTS: The overall bimonthly CSF shunt infection rate over time did not change significantly from 2007 to 2012. There was no difference in the trajectory of infection rates between HCRN and non-HCRN hospitals during the entire study period. No change in distributions of gram-negative organism infections was observed in hospitals from 2007 to 2015. CONCLUSIONS: There were no differences observed in hospital-level infection rates for low-risk patients undergoing CSF shunt surgery. This included analyses based on participation in the HCRN network, given their regular use of intrathecal antibiotics in period 1 and a focus on gram-negative infections with increasing adoption of AICs in period 2.

The impact of regulatory workplace safety inspections on workers' compensation claim rates.

BACKGROUND: Studies on the impact of workplace safety inspections on work injuries have found mixed effectiveness. Most studies are from the United States, examining Occupational Health and Safety Administration (OSHA) regulatory inspections in manufacturing firms with more than 10 employees. This study examines whether regulatory inspections in Alberta, Canada, result in reductions in workers' compensation claims rates for inspected firms relative to comparable non-inspected firms. METHODS: Firm and claim-level data from the Workers' Compensation Board of Alberta were linked with regulatory enforcement data from the Government of Alberta for construction, manufacturing, and transportation firms with at least one full-time employee for 37 consecutive months. A matched difference-in-differences study design was used to estimate changes in lost-time claim rates for work-related injuries and musculoskeletal diseases of inspected and comparable non-inspected firms between the year pre-inspection and 2 years, post-inspection, controlling for firm-level characteristics. RESULTS: Inspections were not effective in reducing firm-level claim rates, with the exception of transportation firms with more than one inspection experiencing a 28% decrease in their claim rate in the second year post-inspection, relative to the change in non-inspected firms. In construction, inspected firms experienced a 12% increase in their claim rate in the first year post-inspection. No effect was observed in the manufacturing sector. CONCLUSIONS: Regulatory workplace safety inspections in Alberta generally do not result in greater reductions in firm-level claim rates in the construction, manufacturing, and transportation sectors. Inspections alone may not be sufficient to induce compliance or hazard management changes that lead to reductions in firm-level injuries.

A Multiplexed Single-Cell Proteomic Workflow Applicable to Drug Treatment Studies.

It is now well accepted that individual cells within a population will respond to treatment of the same drug in a heterogenous manner. Recent advances have allowed, for the first time, the quantitative analysis of the proteomes of single human cells by mass spectrometry. A major focus of many groups, including our own, has been to use this emerging technology to rapidly identify subpopulations of cells with unique drug response and adaptation methods. While the technology in single-cell proteomics today is progressing at a truly staggering rate, we will detail our current methods for applying highly multiplexed single-cell proteomics to drug treatment studies.

May Measurement Month 2021: an analysis of blood pressure screening results from the Philippines.

The Philippine Society of Hypertension (PSH) took part again in the annual May Measurement Month 2021 (MMM21) blood pressure (BP) campaign to raise awareness of hypertension. The MMM standard protocol designed by the MMM coordinating centre was used during screening. These included the collection of basic data on demography, lifestyle, and environmental factors. Standardized sitting BP measurements were taken three times, using automated BP apparatus and were either entered via MMM21 app, MMM@Home, and Google Forms or recorded on paper and transferred to Excel spreadsheets by PSH encoders. Hypertension was defined either as systolic BP ≥ 140 mmHg or diastolic BP ≥ 90 mmHg or on antihypertensive medication. A total of 59 655 participated through opportunistic convenience sampling. After multiple imputation, a total of 26 813 (44.9%) participants were identified as having hypertension. Of these, 14 449 (53.9%) were aware and 12 978 (48.4%) were on antihypertensive medication. Of those who were treated, 5644 (43.5%) had controlled BP (<140/90 mmHg) and 7334 (56.5%) were uncontrolled. These latest local data showed that BP awareness is still low with BP control achieved in less than half of treated patients. Continued collaboration is needed to improve BP screening programmes in the country.